Machine learning is a powerful tool that can be used to analyze data and identify trends, patterns, and outliers. By leveraging this technology with predictive analysis, facility managers can make data-driven decisions that increase the efficiency of their operations. This technology can provide operators with information on how to change equipment schedules to maximize efficiency and reduce operating costs. Data-based decisions in facility management include whether to repair or replace a machine for better results and cost savings, how to allocate expenses for the highest return on investment, and when to visit multiple locations depending on the current state of the facilities.
Machine learning can take thousands of data points from the use of equipment and various sensors to “know the true schedule of a building”. By smoothing out these data points, this technology can provide operators with information on exactly how to change equipment schedules to maximize efficiency and thus reduce operating costs. The algorithms compare this situation with historical data for the same building and other similar buildings to make a prediction about operational needs. Predictive maintenance can use historical data from the labeled machine and current data from the sensors to predict when an asset may need maintenance and what that maintenance would be. With all the advantages that machine learning offers, commercial real estate companies may be wondering if they should develop the technology in-house or hire a supplier. Ultimately, it is up to each company to decide which option is best for them. The potential of machine learning in commercial building maintenance is immense.
By leveraging this technology, facility managers can make informed decisions that increase efficiency and reduce operating costs. Predictive maintenance can use historical data from labeled machines and current data from sensors to predict when an asset may need maintenance and what that maintenance would be. With all these benefits, commercial real estate companies should consider investing in machine learning technology to unlock its full potential.